It is well documented that multimorbidity, or multiple chronic conditions (MCC), impairs patients' functioning and health-related quality of life (QOL). However, our ability to study the impact of and account for multimorbidity is hampered by methodological problems underlying its conceptualization and measurement. These include lack of a uniform definition, proliferation of diverse multimorbidity indexes, widespread reliance on indexes using population weights that were not developed to explain variations in patients' QOL, and a paucity of indexes that take individual assessments of disease burden into account. The goal of this project is to improve the assessment of MCC impact for the purposes of analyzing and interpreting generic patient- reported outcome (PRO) measures of QOL. We will conduct secondary analyses of cross-sectional and longitudinal data collected in 2011 and 2012 for 10,624 adults from two independent samples (one sampled to represent the US general population and a second cohort oversampled from adults pre-identified with arthritis, cardiovascular disease, chronic kidney disease, diabetes or respiratory disease), pursuant to the NIA- sponsored Computerized Adaptive Assessment of Disease Impact (DICAT) grant (R44 AG025589, Ware, PI). For 36 conditions, DICAT developed standardized disease-specific scales measuring the QOL impact attributed to each condition. These condition-specific QOL impact scores have been evaluated on a disease- by-disease basis but have not been evaluated collectively to determine how well they measure multimorbidity and its impact. We propose to extend this work to better understand the ability of patients to make valid QOL attributions to individual conditions in the context of MCC. We will also determine the importance of differences in alternative approaches to aggregating condition-specific patient QOL impact ratings in the presence of MCC, specifically for purposes of analyzing and interpreting generic PROs. The project has the following specific aims: (1) Explore the extent to which patients with MCC can make valid attributions to specific conditions when reporting disease impact on QOL. (2) Systematically evaluate different MCC aggregation methods used to predict generic PRO outcomes. (3) Cross-validate methods and results with independent data. Additional research will likely be required to fully implement the methodological advances proven to be useful in this research. However, the approach to conceptualizing and measuring MCC impact evaluated in this project ultimately may enable the use of standardized PRO-based MCC impact measures to better estimate and interpret differences in patient case-mix and PRO outcomes in comparative effectiveness research.